Triple
T14537764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Pitt |
E341092
|
entity |
| Predicate | relationshipToElaineBenes |
P114834
|
FINISHED |
| Object | employer |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: employer | Statement: [Mr. Pitt, relationshipToElaineBenes, employer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToElaineBenes Context triple: [Mr. Pitt, relationshipToElaineBenes, employer]
-
A.
relationshipStatusWithElaineBenes
Indicates the nature or current state of an entity’s interpersonal or romantic relationship with Elaine Benes.
-
B.
relationshipToBenjy
Indicates the specific type of relationship or connection an entity has to Benjy.
-
C.
relationshipToElaineRisley
Indicates the nature or type of connection an entity has to Elaine Risley, such as familial, social, or professional relationship.
-
D.
relationshipToTinaBordereau
Indicates the specific type of personal or professional relationship an entity has with Tina Bordereau.
-
E.
relationshipWithTobyFlenderson
Indicates that one entity has some form of relationship, connection, or association with Toby Flenderson.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d822dac79c8190a84a073f3cbaced5 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb1bb90008190947ac0961393446d |
completed | April 14, 2026, 9:29 p.m. |
| PD | Predicate disambiguation | batch_69de5c546c7081909e27d504ec360c5c |
completed | April 14, 2026, 3:25 p.m. |
| PDg | Predicate description generation | batch_69de610330a48190b558235a14c0dc9f |
completed | April 14, 2026, 3:45 p.m. |
Created at: April 10, 2026, 1:22 a.m.